Government regulations on fuel economy and emission standards have driven the development of engine technologies that improve engine efficiency. This technology is enabled by an increased number of actuators and more sophisticated control algorithms. As a consequence, powertrain controls steady-state optimization has increased significantly. The steady state optimization may include examining each speed-load point to determine actuator combination settings that meet predefined constraints and optimizes for fuel economy. However, identifying actuator combinations for each speed-load point may be a complex and lengthy process. As an example, extensive dynamometer data collection and post processing may be required to generate actuator settings for each speed-load point. Overall, this exercise can be prolonged, complicated, and can lead to increased costs.
The inventors herein have recognized the above issues and identified an approach to at least partly address some of the above issues. In one example approach, a method for an engine is provided comprising obtaining actuator settings for engine operation at non-boundary conditions of an engine speed-load map for which no adaptive learning is conducted via interpolation from actuator settings adaptively learned during engine operation at boundary conditions of the engine speed-load map.
In one example, an engine may be operated initially (post-manufacture) with preprogrammed settings. As engine operation continues, and boundary conditions on an engine speed-load map are encountered, engine settings for these boundary conditions may be learned. Herein, boundary conditions of the speed-load map may include one or more of minimum speed at any engine load, maximum speed at any engine load, minimum load at any engine speed, and maximum load at any engine speed, or minimum brake specific fuel consumption (BSFC). These learned engine settings may be further adapted for providing desired outputs such as improved fuel economy and reduced emissions. Additionally, these adaptively learned settings may be stored and interpolated to positions in the engine speed-load map for which no adaptive learning was previously (or will be) performed. The interpolation may be accomplished via a model of the engine rather than by using adaptive control schemes across the entire speed-load table at steady state conditions. The accuracy of the interpolation can be determined based on the points actually visited during real-time control. Therefore, rather than using adaptive control schemes across the entire engine speed-load table at steady-state (and thus requiring a visit to each speed-load point to learn data for that point), adaptively learned data at a select sub-set (e.g. boundary conditions) of the speed-load map may be either interpolated or extrapolated to positions in the map for which no adaptive learning was done, using a model of the engine.
Thus, to reduce the complexity in the context of real-time control systems which use look-up tables (LUTs), a hybrid approach for powertrain controls optimization may be utilized. The hybrid approach may combine indirect adaptive control wherein a select few points in the speed-load map (optionally only at the load boundaries) may be visited, with a parallel system identification of a dynamic node look-up table. The dynamic node look-up table may then be used in real time or offline to determine steady-state actuators settings for speed-load points not explicitly visited by the adaptive control. The actuators may include throttle, spark, and intake and exhaust cam timings (including intake valve opening timing, intake valve closing timing, exhaust valve opening timing, and exhaust valve closing timing). The optimization may be of various parameters, such as BSFC, while meeting CA50 (crank angle percentage, e.g., 50%) burn targets and load targets.
In this way, powertrain controls may be optimized without extensive data collection in real time operation. By learning adaptive actuator settings only at selected regions, e.g. at the boundaries of the speed-load map, each speed-load point on the map may not be explicitly visited for gathering data. Therefore, a significant reduction in data collection and post processing may be achieved. Further, since the modeled actuator settings for points within the boundaries of the speed-load map are based on adaptively learned settings for optimized outputs, an improvement in fuel economy and emissions may be attained. Overall, the model may enable a reduction in processing time and an improvement in fuel efficiency.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.